{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 8 Object references, mutability and recycling\n",
    "## Variables are not boxes\n",
    "Python variables are like reference variables in Java, so it’s better to think\n",
    "of them as labels attached to objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Gizmo:\n",
    "    def __init__(self):\n",
    "        print('Gizmo id: %d' % id(self))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gizmo id: 91791880\n"
     ]
    }
   ],
   "source": [
    "x=Gizmo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gizmo id: 91792104\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for *: 'Gizmo' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-c7d161515ba7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mGizmo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'Gizmo' and 'int'"
     ]
    }
   ],
   "source": [
    "y=Gizmo()*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gizmo',\n",
       " 'In',\n",
       " 'Out',\n",
       " '_',\n",
       " '_3',\n",
       " '_4',\n",
       " '__',\n",
       " '___',\n",
       " '__builtin__',\n",
       " '__builtins__',\n",
       " '__doc__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_dh',\n",
       " '_i',\n",
       " '_i1',\n",
       " '_i2',\n",
       " '_i3',\n",
       " '_i4',\n",
       " '_i5',\n",
       " '_i6',\n",
       " '_i7',\n",
       " '_ih',\n",
       " '_ii',\n",
       " '_iii',\n",
       " '_oh',\n",
       " 'exit',\n",
       " 'get_ipython',\n",
       " 'quit',\n",
       " 'x']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand an assignment in Python, always read the right-hand\n",
    "side first: that’s where the object is created or retrieved. After that, the\n",
    "variable on the left is bound to the object, like a label stuck to it. Just\n",
    "forget about the boxes.\n",
    "## Identity, equality and aliases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charles = {'name': 'Charles L. Dodgson', 'born': 1832}\n",
    "lewis = charles\n",
    "lewis is charles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91771024"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(charles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91771024"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(lewis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'balance': 500, 'born': 1832, 'name': 'Charles L. Dodgson'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lewis['balance']=500\n",
    "charles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alex = {'name': 'Charles L. Dodgson', 'born': 1832, 'balance': 500}\n",
    "alex==lewis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alex is lewis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every object has an identity, a type and a value. An object’s identity never changes once\n",
    "it has been created; you may think of it as the object’s address in memory. The is operator\n",
    "compares the identity of two objects; the id() function returns an integer representing\n",
    "its identity.\n",
    "### Choosing between == and is\n",
    "The == operator compares the values of objects (the data they hold), while is compares\n",
    "their identities.However, if you are comparing a variable to a singleton, then it makes sense to use is.\n",
    "By far, the most common case is checking whether a variable is bound to None. This is\n",
    "the recommended way to do it:\n",
    "x is None\n",
    "And the proper way to write its negation is:\n",
    "x is not None\n",
    "\n",
    "### The relative immutability of tuples\n",
    "Tuples, like most Python collections — lists, dicts, sets etc. — hold references to objects2.\n",
    "If the referenced items are mutable, they may change even if the tuple itself does\n",
    "not. In other words, the immutability of tuples really refers to the physical contents of\n",
    "the tuple data structure (ie. the references it holds), and does not extend to the referenced\n",
    "objects\n",
    "\n",
    "On the other hand, single-type sequences like str, bytes and array.array are flat: they don’t contain\n",
    "references but physically hold their data — characters, bytes and numbers — in contiguous memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = (1, 2, [30, 40])\n",
    "t2 = (1, 2, [30, 40])\n",
    "t1 == t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91786952"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(t1[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t1[-1].append(99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91786952, (1, 2, [30, 40, 99]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(t1[-1]),t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1==t2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copies are shallow by default\n",
    "The easiest way to copy a list (or most built-in mutable collections) is to use the builtin\n",
    "constructor for the type itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, [55, 44], (7, 8, 9)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = [3, [55, 44], (7, 8, 9)]\n",
    "l2 = list(l1)\n",
    "l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2==l1, l2 is l1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For lists and other mutable sequences, the shortcut l2 = l1[:] also makes a copy\n",
    "However, using the constructor or [:] produces a shallow copy, i.e. the outermost container\n",
    "is duplicated, but the copy is filled with references to the same items held by the\n",
    "original container. This saves memory and causes no problems if all the items are immutable.\n",
    "But if there are mutable items, this may lead to unpleasant surprises.\n",
    "It should be clear now that shallow copies are easy to make, but they may or may not\n",
    "be what you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, [55, 44], (7, 8, 9)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l3=l1[:]\n",
    "l3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, [66, 55, 44], (7, 8, 9)] [3, [66, 55, 44], (7, 8, 9)]\n",
      "l1: [3, [66, 44], (7, 8, 9), 100]\n",
      "l2: [3, [66, 44], (7, 8, 9)]\n",
      "l1: [3, [66, 44, 33, 22], (7, 8, 9), 100]\n",
      "l2: [3, [66, 44, 33, 22], (7, 8, 9, 10, 11)]\n"
     ]
    }
   ],
   "source": [
    "l1 = [3, [66, 55, 44], (7, 8, 9)]\n",
    "l2 = list(l1) #\n",
    "print(l1,l2)\n",
    "l1.append(100) #\n",
    "l1[1].remove(55) #\n",
    "print('l1:', l1)\n",
    "print('l2:', l2)\n",
    "l2[1] += [33, 22] #\n",
    "l2[2] += (10, 11) #\n",
    "print('l1:', l1)\n",
    "print('l2:', l2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep and shallow copies of arbitrary objects\n",
    "Working with shallow copies is not always a problem, but sometimes you need to make\n",
    "deep copies, i.e. duplicates that do not share references of embedded objects.\n",
    "The copy module provides the deepcopy and copy functions that return deep and shallow copies\n",
    "of arbitrary objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Bus:\n",
    "    def __init__(self, passengers=None):\n",
    "        if passengers is None:\n",
    "            self.passengers = []\n",
    "        else:\n",
    "            self.passengers = list(passengers)\n",
    "    def pick(self, name):\n",
    "        self.passengers.append(name)\n",
    "    def drop(self, name):\n",
    "        self.passengers.remove(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['Alice', 'Claire', 'David'],\n",
       " ['Alice', 'Claire', 'David'],\n",
       " ['Alice', 'Bill', 'Claire', 'David'])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import copy\n",
    "bus1 = Bus(['Alice', 'Bill', 'Claire', 'David'])\n",
    "bus2 = copy.copy(bus1)\n",
    "bus3 = copy.deepcopy(bus1)\n",
    "id(bus1), id(bus2), id(bus3)\n",
    "bus1.drop('Bill')\n",
    "bus1.passengers,bus2.passengers,bus3.passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92399624, 92399624, 92400840)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(bus1.passengers), id(bus2.passengers), id(bus3.passengers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function parameters as references\n",
    "The only mode of parameter passing in Python is call by sharing. That is the same mode\n",
    "used in most OO languages, including Ruby, SmallTalk and Java3. Call by sharing means\n",
    "that each formal parameter of the function gets a copy of each reference in the arguments.\n",
    "In other words, the parameters inside the function become aliases of the actual\n",
    "arguments.\n",
    "The result of this scheme is that a function may change any mutable object passed as a\n",
    "parameter, but it cannot change the identity of those objects, i.e. it cannot replace altogether\n",
    "an object with another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(a, b):\n",
    "    a += b\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1, 2)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 1\n",
    "y = 2\n",
    "f(x,y),x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1, 2, 3, 4], [1, 2, 3, 4], [3, 4])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1, 2]\n",
    "b = [3, 4]\n",
    "f(a, b),a,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10, 20, 30, 40), (10, 20), (30, 40))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = (10, 20)\n",
    "u = (30, 40)\n",
    "f(t, u),t,u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutable types as parameter defaults: bad idea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class HauntedBus:\n",
    "    \"\"\"A bus model haunted by ghost passengers\"\"\"\n",
    "    def __init__(self, passengers=[]):\n",
    "        self.passengers = passengers\n",
    "    def pick(self, name):\n",
    "        self.passengers.append(name)\n",
    "    def drop(self, name):\n",
    "        self.passengers.remove(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice', 'Bill']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus1 = HauntedBus(['Alice', 'Bill'])\n",
    "bus1.passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bill', 'Charlie']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus1.pick('Charlie')\n",
    "bus1.drop('Alice')\n",
    "bus1.passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Carrie']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus2 = HauntedBus()\n",
    "bus2.pick('Carrie')\n",
    "bus2.passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Carrie']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus3 = HauntedBus()\n",
    "bus3.passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bus3.pick('Dave')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Carrie', 'Dave']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus2.passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus2.passengers is bus3.passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bill', 'Charlie']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus1.passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strange things happen only when a\n",
    "HauntedBus starts empty, because then self.passengers becomes an alias for the default\n",
    "value of the passengers parameter.\n",
    "if a default value is a mutable\n",
    "object, and you change it, the change will affect every future call of the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__annotations__',\n",
       " '__call__',\n",
       " '__class__',\n",
       " '__closure__',\n",
       " '__code__',\n",
       " '__defaults__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__get__',\n",
       " '__getattribute__',\n",
       " '__globals__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__kwdefaults__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__name__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__qualname__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(HauntedBus.__init__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['Carrie', 'Dave'],)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HauntedBus.__init__.__defaults__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HauntedBus.__init__.__defaults__[0] is bus2.passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The issue with mutable defaults explains why None is often used as the default value for\n",
    "parameters that may receive mutable values.\n",
    "### Defensive programming with mutable parameters\n",
    "When you are coding a function that receives a mutable parameter you should carefully\n",
    "consider whether the caller expects the argument passed to be changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class TwilightBus:\n",
    "    \"\"\"A bus model that makes passengers vanish\"\"\"\n",
    "    def __init__(self, passengers=None):\n",
    "        if passengers is None:\n",
    "            self.passengers = []\n",
    "        else:\n",
    "            self.passengers = passengers\n",
    "    def pick(self, name):\n",
    "        self.passengers.append(name)\n",
    "    def drop(self, name):\n",
    "        self.passengers.remove(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sue', 'Maya', 'Diana']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basketball_team = ['Sue', 'Tina', 'Maya', 'Diana', 'Pat']\n",
    "bus = TwilightBus(basketball_team)\n",
    "bus.drop('Tina')\n",
    "bus.drop('Pat')\n",
    "basketball_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class TwilightBus:\n",
    "    \"\"\"A bus model that makes passengers vanish\"\"\"\n",
    "    def __init__(self, passengers=None):\n",
    "        if passengers is None:\n",
    "            self.passengers = []\n",
    "        else:\n",
    "            self.passengers = list(passengers)\n",
    "    def pick(self, name):\n",
    "        self.passengers.append(name)\n",
    "    def drop(self, name):\n",
    "        self.passengers.remove(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['Sue', 'Maya', 'Diana'], ['Sue', 'Tina', 'Maya', 'Diana', 'Pat'])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basketball_team = ['Sue', 'Tina', 'Maya', 'Diana', 'Pat']\n",
    "bus = TwilightBus(basketball_team)\n",
    "bus.drop('Tina')\n",
    "bus.drop('Pat')\n",
    "bus.passengers,basketball_team"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unless a method is explicitly intended to mutate an object received\n",
    "as argument, you should think twice before aliasing the argument\n",
    "object by simply assigning it to an instance variable in your class. If\n",
    "in doubt, make a copy. Your clients will often be happier.\n",
    "## del and garbage collection\n",
    "The del statement deletes names, not objects. An object may be garbage collected as\n",
    "result of a del command, but only if the variable deleted holds the last reference to the\n",
    "object, or if the object becomes unreachable4. Rebinding a variable may also cause the\n",
    "number of references to an object reach zero, causing its destruction.\n",
    "There is a __del__ special method, but it does not cause the disposal\n",
    "of the instance, and should not be called by your code. __del__ is\n",
    "invoked by the Python interpreter when the instance is about to be\n",
    "destroyed to give it a chance to release external resources. You will\n",
    "seldom need to implement __del__ in your own code, yet some\n",
    "Python beginners spend time coding it for no good reason. The\n",
    "proper use of __del__ is rather tricky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import weakref\n",
    "s1 = {1, 2, 3}\n",
    "s2 = s1\n",
    "def bye():\n",
    "    print('Gone with the wind...')\n",
    "ender = weakref.finalize(s1, bye)\n",
    "ender.alive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del s1\n",
    "ender.alive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gone with the wind...\n"
     ]
    }
   ],
   "source": [
    "s2 = 'spam'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ender.alive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### class weakref.finalize(obj, func, *args, **kwargs)\n",
    "Return a callable finalizer object which will be called when obj is garbage collected. Unlike an ordinary weak reference, a finalizer will always survive until the reference object is collected, greatly simplifying lifecycle management.\n",
    "\n",
    "A finalizer is considered alive until it is called (either explicitly or at garbage collection), and after that it is dead. Calling a live finalizer returns the result of evaluating func(*arg, **kwargs), whereas calling a dead finalizer returns None.\n",
    "## Weak references\n",
    "\n",
    "sometimes it is useful to have a reference to an object that does not keep it around longer than necessary.\n",
    "A common use case is a cache.\n",
    "\n",
    "Weak references to an object do not increase its reference count. The object that is the\n",
    "target of a reference is called the referent. Therefore, we say that a weak reference does\n",
    "not prevent the referent from being garbage collected.\n",
    "\n",
    "Weak references are useful in caching applications because you don’t want the cached\n",
    "objects to be kept alive just because they are referenced by the cache.\n",
    "\n",
    "a weakref.ref instance can be called to reach its referent. If\n",
    "the object is alive, calling the weak reference returns it, otherwise None is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<weakref at 0x000000000583D098; to 'set' at 0x00000000057822E8>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import weakref\n",
    "a_set = {0, 1}\n",
    "wref = weakref.ref(a_set)\n",
    "wref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wref()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a_set = {2, 3, 4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wref()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wref() is None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wref() is None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The WeakValueDictionary skit\n",
    "The class WeakValueDictionary implements a mutable mapping where the values are\n",
    "weak references to objects. When a referred object is garbage collected elsewhere in the\n",
    "program, the corresponding key is automatically removed from the WeakValueDiction\n",
    "ary. This is commonly used for caching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Cheese:\n",
    "    def __init__(self, kind):\n",
    "        self.kind = kind\n",
    "    def __repr__(self):\n",
    "        return 'Cheese(%r)' % self.kind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Brie', 'Parmesan', 'Red Leicester', 'Tilsit']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import weakref\n",
    "stock = weakref.WeakValueDictionary()\n",
    "catalog = [Cheese('Red Leicester'), Cheese('Tilsit'),\n",
    "    Cheese('Brie'), Cheese('Parmesan')]\n",
    "for cheese in catalog:\n",
    "    stock[cheese.kind] = cheese\n",
    "sorted(stock.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Parmesan']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del catalog\n",
    "sorted(stock.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del cheese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(stock.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[A WeakKeyDictionary] can be used to associate additional data with an object owned\n",
    "by other parts of an application without adding attributes to those objects. This can be\n",
    "especially useful with objects that override attribute accesses.\n",
    "The weakref module also provides a WeakSet, simply described in the docs as “Set class\n",
    "that keeps weak references to its elements. An element will be discarded when no strong\n",
    "reference to it exists any more.” If you need to build a class that is aware of every one of\n",
    "its instances, a good solution is to create a class attribute with a WeakSet to hold the\n",
    "references to the instances. Otherwise, if a regular set was used, the instances would\n",
    "never be garbage collected, because the class itself would have strong references to them,\n",
    "and classes live as long as the Python process unless you deliberately delete them.\n",
    "### Limitations of weak references\n",
    "Not every Python object may be the target, or referent, of a weak reference. Basic list\n",
    "and dict instances may not be referents, but a plain subclass of either can solve this\n",
    "problem easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MyList(list):\n",
    "    \"\"\"list subclass whose instances may be weakly referenced\"\"\"\n",
    "a_list = MyList(range(10))\n",
    "wref_to_a_list = weakref.ref(a_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tricks Python plays with immutables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = (1, 2, 3)\n",
    "t2 = tuple(t1)\n",
    "t2 is t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = t1[:]\n",
    "t3 is t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = (1, 2, 3)\n",
    "t3 = (1, 2, 3) #\n",
    "t3 is t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = 'ABC'\n",
    "s2 = 'ABC' #\n",
    "s2 is s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sharing of string literals is an optimization technique called interning. CPython\n",
    "uses the same technique with small integers to avoid unnecessary duplication of “popular”\n",
    "numbers like 0, -1 and 42. Please note that CPython does not intern all strings or\n",
    "integers, and the criteria it uses to do so is an undocumented implementation detail.\n",
    "\n",
    "Never depend on str or int interning! Always use == and not\n",
    "is to compare them for equality. Interning is a feature for\n",
    "internal use of the Python interpreter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 9 A Pythonic object\n",
    "## Object representations\n",
    "Every object-oriented language has at least one standard way of getting a string representation\n",
    "from any object. Python has two:\n",
    "repr()\n",
    "Return a string representing the object as the developer wants to see it.\n",
    "str()\n",
    "Return a string representing the object as the user wants to see it.\n",
    "As you know, we implement the special methods __repr__ and __str__ to support\n",
    "repr() and str().\n",
    "There are two additional special methods to support alternate representations of objects:\n",
    "__bytes__ and __format__. The __bytes__ method is analogous to __str__: it’s called\n",
    "by bytes() to get the object represented as a byte sequence. Regarding __format__,\n",
    "both the built-in function format() and the str.format() method call it to get string\n",
    "displays of objects using special formatting codes.\n",
    "## Vector class redux\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from array import array\n",
    "import math\n",
    "class Vector2d:\n",
    "    typecode = 'd'\n",
    "    def __init__(self, x, y):\n",
    "        self.x = float(x)\n",
    "        self.y = float(y)\n",
    "    def __iter__(self):\n",
    "        return (i for i in (self.x, self.y))\n",
    "    def __repr__(self):\n",
    "        class_name = type(self).__name__\n",
    "        return '{}({!r}, {!r})'.format(class_name, *self)\n",
    "    def __str__(self):\n",
    "        return str(tuple(self))\n",
    "    def __bytes__(self):\n",
    "        return (bytes([ord(self.typecode)]) +\n",
    "            bytes(array(self.typecode, self)))\n",
    "    def __eq__(self, other):\n",
    "        return tuple(self) == tuple(other)\n",
    "    def __abs__(self):\n",
    "        return math.hypot(self.x, self.y)  ## 返回欧几里德范数\n",
    "    def __bool__(self):\n",
    "        return bool(abs(self))\n",
    "    @classmethod\n",
    "    def frombytes(cls, octets): \n",
    "        typecode = chr(octets[0])\n",
    "        memv = memoryview(octets[1:]).cast(typecode)\n",
    "        return cls(*memv)\n",
    "    def angle(self):\n",
    "        return math.atan2(self.y, self.x) # 返回给定的 X 及 Y 坐标值的反正切值。\n",
    "    def __format__(self, fmt_spec=''):\n",
    "        if fmt_spec.endswith('p'):\n",
    "            fmt_spec = fmt_spec[:-1]\n",
    "            coords = (abs(self), self.angle())\n",
    "            outer_fmt = '<{}, {}>'\n",
    "        else:\n",
    "            coords = self\n",
    "            outer_fmt = '({}, {})'\n",
    "        components = (format(c, fmt_spec) for c in coords)\n",
    "        return outer_fmt.format(*components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<1.4142135623730951, 0.7853981633974483>'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(Vector2d(1, 1), 'p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<1.414e+00, 7.854e-01>'"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(Vector2d(1, 1), '.3ep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<1.41421, 0.78540>'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(Vector2d(1, 1), '0.5fp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0 4.0\n"
     ]
    }
   ],
   "source": [
    "v1 = Vector2d(3, 4)\n",
    "print(v1.x, v1.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.0, 4.0)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = v1\n",
    "x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vector2d(3.0, 4.0)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1_clone = eval(repr(v1))\n",
    "v1 == v1_clone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3.0, 4.0)\n"
     ]
    }
   ],
   "source": [
    "print(v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'd\\x00\\x00\\x00\\x00\\x00\\x00\\x08@\\x00\\x00\\x00\\x00\\x00\\x00\\x10@'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "octets = bytes(v1)\n",
    "octets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(v1), bool(Vector2d(0, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An alternative constructor\n",
    "\n",
    "## classmethod versus staticmethod\n",
    "Let’s start with classmethod. Example 9-3 shows its use: to define a method that operates\n",
    "on the class and not on instances. classmethod changes the way the method is called,\n",
    "so it receives the class itself as the first argument, instead of an instance.\n",
    "\n",
    "In contrast, the staticmethod decorator changes a method so that it receives no special\n",
    "first argument. In essence, a static method is just like a plain function that happens to\n",
    "live in a class body, instead of being defined at the module level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Demo:\n",
    "    @classmethod\n",
    "    def klassmeth(*args):\n",
    "        return args #\n",
    "    @staticmethod\n",
    "    def statmeth(*args):\n",
    "        return args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(__main__.Demo,)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demo.klassmeth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(__main__.Demo, 'spam')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demo.klassmeth('spam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demo.statmeth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('spam',)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demo.statmeth('spam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formatted displays\n",
    "The format() built-in function and the str.format() method delegate the actual formatting\n",
    "to each type by calling their .__format__(format_spec) method. The for\n",
    "mat_spec is a formatting specifier, which is either:\n",
    "• The second argument in format(my_obj, format_spec), or\n",
    "• whatever appears after the colon in a replacement field delimited with {} inside a\n",
    "format string used with str.format()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'101010'"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(42, 'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'66.7%'"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(2/3, '.1%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'08:30:57'"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "now = datetime.now()\n",
    "format(now, '%H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"It's now 08:30 AM\""
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"It's now {:%I:%M %p}\".format(now)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A hashable Vector2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Vector2d:\n",
    "    typecode = 'd'\n",
    "    def __init__(self, x, y):\n",
    "        self.__x = float(x)\n",
    "        self.__y = float(y)\n",
    "    @property #The @property decorator marks the getter method of a property.\n",
    "\n",
    "    def x(self):\n",
    "        return self.__x\n",
    "    @property\n",
    "    def y(self):\n",
    "        return self.__y\n",
    "    def __iter__(self):\n",
    "        return (i for i in (self.x, self.y))\n",
    "    def __hash__(self):\n",
    "        return hash(self.x) ^ hash(self.y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-9223372036848840602, -9223372036848840609)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = Vector2d(3, 4)\n",
    "v2 = Vector2d(3.1, 4.2)\n",
    "hash(v1), hash(v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{<__main__.Vector2d at 0x5a90668>, <__main__.Vector2d at 0x5a905f8>}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([v1, v2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from array import array   #v3\n",
    "import math\n",
    "\n",
    "class Vector2d:\n",
    "    typecode = 'd'\n",
    "\n",
    "    def __init__(self, x, y):\n",
    "        self.__x = float(x)\n",
    "        self.__y = float(y)\n",
    "\n",
    "    @property\n",
    "    def x(self):\n",
    "        return self.__x\n",
    "\n",
    "    @property\n",
    "    def y(self):\n",
    "        return self.__y\n",
    "\n",
    "    def __iter__(self):\n",
    "        return (i for i in (self.x, self.y))\n",
    "\n",
    "    def __repr__(self):\n",
    "        class_name = type(self).__name__\n",
    "        return '{}({!r}, {!r})'.format(class_name, *self)\n",
    "\n",
    "    def __str__(self):\n",
    "        return str(tuple(self))\n",
    "\n",
    "    def __bytes__(self):\n",
    "        return (bytes([ord(self.typecode)]) +\n",
    "                bytes(array(self.typecode, self)))\n",
    "\n",
    "    def __eq__(self, other):\n",
    "        return tuple(self) == tuple(other)\n",
    "\n",
    "    def __hash__(self):\n",
    "        return hash(self.x) ^ hash(self.y)\n",
    "\n",
    "    def __abs__(self):\n",
    "        return math.hypot(self.x, self.y)\n",
    "\n",
    "    def __bool__(self):\n",
    "        return bool(abs(self))\n",
    "\n",
    "    def angle(self):\n",
    "        return math.atan2(self.y, self.x)\n",
    "\n",
    "    def __format__(self, fmt_spec=''):\n",
    "        if fmt_spec.endswith('p'):\n",
    "            fmt_spec = fmt_spec[:-1]\n",
    "            coords = (abs(self), self.angle())\n",
    "            outer_fmt = '<{}, {}>'\n",
    "        else:\n",
    "            coords = self\n",
    "            outer_fmt = '({}, {})'\n",
    "        components = (format(c, fmt_spec) for c in coords)\n",
    "        return outer_fmt.format(*components)\n",
    "\n",
    "    @classmethod\n",
    "    def frombytes(cls, octets):\n",
    "        typecode = chr(octets[0])\n",
    "        memv = memoryview(octets[1:]).cast(typecode)\n",
    "        return cls(*memv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Private and “protected” attributes in Python\n",
    "In Python there is no way to create private variables in the strong sense of the pri\n",
    "vate modifier in Java. What we have in Python is a simple mechanism to prevent accidental\n",
    "overwriting of a “private” attribute in a subclass.\n",
    "To prevent this, if you name an instance attribute in the form __mood (two leading\n",
    "underscores and zero or at most one trailing underscore), Python stores the name in\n",
    "the instance __dict__ prefixed with a leading underscore and the class name, so in the\n",
    "Dog class, __mood becomes _Dog__mood, and in Beagle it’s _Beagle__mood. This language\n",
    "feature goes by the lovely name of name mangling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_Vector2d__x': 3.0, '_Vector2d__y': 4.0}"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1=Vector2d(3,4)\n",
    "v1.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1._Vector2d__x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name mangling is about safety, not security: it’s designed to prevent accidental access\n",
    "and not intentional wrongdoing\n",
    "\n",
    "Attributes with a single _ prefix are called “protected” in some corners of the Python\n",
    "documentation9. The practice of “protecting” attributes by convention with the form\n",
    "self._x is widespread, but calling that a “protected” attribute is not so common. Some\n",
    "even call that a “private” attribute.\n",
    "## Saving space with the __slots__ class attribute\n",
    "By default, Python stores instance attributes in a per-instance dict named __dict__.\n",
    "As we saw in “Practical consequences of how dict works” on page 90, dictionaries have\n",
    "a significant memory overhead because of the underlying hash table used to provide\n",
    "fast access. If you are dealing with millions of instances with few attributes, the\n",
    "__slots__ class attribute can save a lot of memory, by letting the interpreter store the\n",
    "instance attributes in a tuple instead of a dict.\n",
    "\n",
    "By defining __slots__ in the class, you are telling the interpreter: “These are all the\n",
    "instance attributes in this class.” Python then stores them in a tuple-like structure in\n",
    "each instance, avoiding the memory overhead of the per-instance __dict__. This can\n",
    "make a huge difference in memory usage if your have millions of instances active at the\n",
    "same time.\n",
    "\n",
    "## Overriding class attributes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'd\\x9a\\x99\\x99\\x99\\x99\\x99\\xf1?\\x9a\\x99\\x99\\x99\\x99\\x99\\x01@'"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = Vector2d(1.1, 2.2)\n",
    "dumpd = bytes(v1)\n",
    "dumpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dumpd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'f\\xcd\\xcc\\x8c?\\xcd\\xcc\\x0c@'"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1.typecode='f'\n",
    "dumpf = bytes(v1)\n",
    "dumpf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dumpf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vector2d.typecode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to change a class attribute you must set it on the class directly, not through\n",
    "an instance.\n",
    "\n",
    "However, there is an idiomatic Python way of achieving a more permanent effect, and\n",
    "being more explicit about the change. Since class attributes are public, they are inherited\n",
    "by subclasses, so it’s common practice to subclass just to customize a class data attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ShortVector2d(Vector2d): #\n",
    "    typecode = 'f'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShortVector2d(0.09090909090909091, 0.037037037037037035)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv = ShortVector2d(1/11, 1/27)\n",
    "sv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bytes(sv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 10 Sequence hacking, hashing and slicing\n",
    "## Vector: a user-defined sequence type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#v1\n",
    "from array import array \n",
    "import reprlib\n",
    "import math\n",
    "class Vector:\n",
    "    typecode = 'd'\n",
    "    def __init__(self, components):\n",
    "        self._components = array(self.typecode, components)\n",
    "    def __iter__(self):\n",
    "        return iter(self._components)\n",
    "    def __repr__(self):\n",
    "        components = reprlib.repr(self._components)\n",
    "        components = components[components.find('['):-1]\n",
    "        return 'Vector({})'.format(components)\n",
    "    def __str__(self):\n",
    "        return str(tuple(self))\n",
    "    def __bytes__(self):\n",
    "        return (bytes([ord(self.typecode)]) +\n",
    "            bytes(self._components))\n",
    "    def __eq__(self, other):\n",
    "        return tuple(self) == tuple(other)\n",
    "    def __abs__(self):\n",
    "        return math.sqrt(sum(x * x for x in self))\n",
    "    def __bool__(self):\n",
    "        return bool(abs(self))\n",
    "    @classmethod\n",
    "    def frombytes(cls, octets):\n",
    "        typecode = chr(octets[0])\n",
    "        memv = memoryview(octets[1:]).cast(typecode)\n",
    "        return cls(memv)\n",
    "    def __len__(self):\n",
    "        return len(self._components)\n",
    "    def __getitem__(self, index):\n",
    "        return self._components[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protocols and duck typing\n",
    "In the context of Object Oriented Programming, a protocol is an informal interface,\n",
    "defined only in documentation and not in code.\n",
    "## Vector take #2: a sliceable sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = Vector([3, 4, 5])\n",
    "len(v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.0, 5.0)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1[0], v1[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('d', [1.0, 2.0, 3.0])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v7 = Vector(range(7))\n",
    "v7[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How slicing works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MySeq:\n",
    "    def __getitem__(self, index):\n",
    "        return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = MySeq()\n",
    "s[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slice(1, 4, None)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slice(1, 4, 2)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:4:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(slice(1, 4, 2), 9)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:4:2, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(slice(1, 4, 2), slice(7, 9, None))"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:4:2, 7:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slice"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'indices',\n",
       " 'start',\n",
       " 'step',\n",
       " 'stop']"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method_descriptor:\n",
      "\n",
      "indices(...)\n",
      "    S.indices(len) -> (start, stop, stride)\n",
      "    \n",
      "    Assuming a sequence of length len, calculate the start and stop\n",
      "    indices, and the stride length of the extended slice described by\n",
      "    S. Out of bounds indices are clipped in a manner consistent with the\n",
      "    handling of normal slices.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(slice.indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 5, 2)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice(None, 10, 2).indices(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5, 1)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice(-3, None, None).indices(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A slice-aware __getitem__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#v2\n",
    "from array import array \n",
    "import reprlib\n",
    "import math\n",
    "import numbers\n",
    "import functools #\n",
    "import operator\n",
    "import itertools\n",
    "class Vector:\n",
    "    typecode = 'd'\n",
    "    def __init__(self, components):\n",
    "        self._components = array(self.typecode, components)\n",
    "    def __iter__(self):\n",
    "        return iter(self._components)\n",
    "    def __repr__(self):\n",
    "        components = reprlib.repr(self._components)\n",
    "        components = components[components.find('['):-1]\n",
    "        return 'Vector({})'.format(components)\n",
    "    def __str__(self):\n",
    "        return str(tuple(self))\n",
    "    def __bytes__(self):\n",
    "        return (bytes([ord(self.typecode)]) +\n",
    "            bytes(self._components))\n",
    "    def __abs__(self):\n",
    "        return math.sqrt(sum(x * x for x in self))\n",
    "    def __bool__(self):\n",
    "        return bool(abs(self))\n",
    "    @classmethod\n",
    "    def frombytes(cls, octets):\n",
    "        typecode = chr(octets[0])\n",
    "        memv = memoryview(octets[1:]).cast(typecode)\n",
    "        return cls(memv)\n",
    "    def __len__(self):\n",
    "        return len(self._components)\n",
    "    def __getitem__(self, index):\n",
    "        cls = type(self)\n",
    "        if isinstance(index, slice):\n",
    "            return cls(self._components[index])\n",
    "        elif isinstance(index, numbers.Integral):\n",
    "            return self._components[index]\n",
    "        else:\n",
    "            msg = '{cls.__name__} indices must be integers'\n",
    "            raise TypeError(msg.format(cls=cls))\n",
    "    #v3 add\n",
    "    shortcut_names = 'xyzt'\n",
    "    def __getattr__(self, name):\n",
    "        cls = type(self)\n",
    "        if len(name) == 1:\n",
    "            pos = cls.shortcut_names.find(name)\n",
    "            if 0 <= pos < len(self._components):\n",
    "                return self._components[pos]\n",
    "        msg = '{.__name__!r} object has no attribute {!r}'\n",
    "        raise AttributeError(msg.format(cls, name))\n",
    "    #v3 add2\n",
    "    def __setattr__(self, name, value):\n",
    "        cls = type(self)\n",
    "        if len(name) == 1:\n",
    "            if name in cls.shortcut_names:\n",
    "                error = 'readonly attribute {attr_name!r}'\n",
    "            elif name.islower():\n",
    "                error = \"can't set attributes 'a' to 'z' in {cls_name!r}\"\n",
    "            else:\n",
    "                error = ''\n",
    "            if error:\n",
    "                msg = error.format(cls_name=cls.__name__, attr_name=name)\n",
    "                raise AttributeError(msg)\n",
    "        super().__setattr__(name, value)#The super() function provides a way to access methods of superclasses\n",
    "                                        #dynamically, a necessity in a dynamic language supporting\n",
    "                                        #multiple inheritance like Python.\n",
    "                \n",
    "    #v4 add \n",
    "    def __eq__(self, other):\n",
    "        return len(self) == len(other) and all(a == b for a, b in zip(self, other))\n",
    "    def __hash__(self):\n",
    "        hashes = (hash(x) for x in self._components) #\n",
    "        return functools.reduce(operator.xor, hashes, 0)\n",
    "    #v5 add \n",
    "    def angle(self, n):\n",
    "        r = math.sqrt(sum(x * x for x in self[n:]))\n",
    "        a = math.atan2(r, self[n-1])\n",
    "        if (n == len(self) - 1) and (self[-1] < 0):\n",
    "            return math.pi * 2 - a\n",
    "        else:\n",
    "            return a\n",
    "    def angles(self):\n",
    "        return (self.angle(n) for n in range(1, len(self)))\n",
    "    def __format__(self, fmt_spec=''):\n",
    "        if fmt_spec.endswith('h'): # hyperspherical coordinates\n",
    "            fmt_spec = fmt_spec[:-1]\n",
    "            coords = itertools.chain([abs(self)],self.angles())\n",
    "            outer_fmt = '<{}>'\n",
    "        else:\n",
    "            coords = self\n",
    "            outer_fmt = '({})'\n",
    "        components = (format(c, fmt_spec) for c in coords)\n",
    "        return outer_fmt.format(', '.join(components))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v7 = Vector(range(7))\n",
    "v7[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vector([1.0, 2.0, 3.0])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v7[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vector([6.0])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v7[-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Vector indices must be integers",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-174-82fccfd8982a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mv7\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-170-0067a0579270>\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'{cls.__name__} indices must be integers'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: Vector indices must be integers"
     ]
    }
   ],
   "source": [
    "v7[1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector take #3: dynamic attribute access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 2.0, 3.0)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = Vector(range(10))\n",
    "v.x,v.y, v.z, v.t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vector([0.0, 1.0, 2.0, 3.0, 4.0])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = Vector(range(5))\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, Vector([0.0, 1.0, 2.0, 3.0, 4.0]))"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.x=10\n",
    "v.x,v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inconsistency in Example 10-9 was introduced because of the way __getattr__\n",
    "works: Python only calls that method as a fall back, when the object does not have the\n",
    "named attribute.\n",
    "\n",
    "On the other hand, our implementation of __getattr__ pays no attention to instance attributes other than self._components, from\n",
    "where it retrieves the values of the “virtual attributes” listed in shortcut_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v = Vector(range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "readonly attribute 'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-194-7392e26afae2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-190-a56f0ef99a11>\u001b[0m in \u001b[0;36m__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m     63\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m                 \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: readonly attribute 'x'"
     ]
    }
   ],
   "source": [
    "v.x=10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector take #4: hashing and a faster ==\n",
    "So far we’ve seen that functools.reduce() can be replaced by sum(), but now let’s\n",
    "properly explain how it works. The key idea is to reduce a series of values to a single\n",
    "value. The first argument to reduce() is a two-argument function, and the second\n",
    "argument is an iterable. Let’s say we have a two-argument function fn and a list lst.\n",
    "When you call reduce(fn, lst), fn will be applied to the first pair of elements —\n",
    "fn(lst[0], lst[1]) — producing a first result, r1. Then fn is applied to r1 and the\n",
    "next element — fn(r1, lst[2]) — producing a second result r2. Now fn(r2,\n",
    "lst[3]) is called to produce r3 … and so on until the last element, when a single result\n",
    "rN is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import functools\n",
    "functools.reduce(lambda a,b: a*b, range(1, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 0\n",
    "for i in range(1, 6): #\n",
    "    n ^= i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import functools\n",
    "functools.reduce(lambda a, b: a^b, range(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import operator\n",
    "functools.reduce(operator.xor, range(6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector take #5: formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<2.0, 2.0943951023931957, 2.186276035465284, 3.9269908169872414>'"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(Vector([-1, -1, -1, -1]), 'h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<4.000e+00, 1.047e+00, 9.553e-01, 7.854e-01>'"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(Vector([2, 2, 2, 2]), '.3eh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<1.00000, 1.57080, 0.00000, 0.00000>'"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(Vector([0, 1, 0, 0]), '0.5fh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### itertools.chain(*iterables)¶\n",
    "Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 11 Interfaces: from protocols to ABCs\n",
    "An abstract class represents an interface.\n",
    "## Interfaces and protocols in Python culture\n",
    "protocols are defined as the informal interfaces that make polymorphism work in languages with dynamic typing\n",
    "like Python.\n",
    "How do interfaces work in a dynamic-typed language? First, the basics: even without\n",
    "an interface keyword in the language, and regardless of ABCs, every class has an\n",
    "interface: the set public attributes (methods or data attributes) implemented or inherited\n",
    "by the class. This includes special methods, like __getitem__ or __add__.\n",
    "By definition, protected and private attributes are not part of an interface, even if “protected”\n",
    "is merely a naming convention (the single leading underscore) and private attributes\n",
    "are easily accessed\n",
    "On the other hand, it’s not a sin to have public data attributes as part of the interface of\n",
    "an object\n",
    "A useful complementary definition of interface is: the subset of an object’s public methods\n",
    "that enable it to play a specific role in the system.\n",
    "Protocols are interfaces, but because they are informal — defined only by documentation\n",
    "and conventions — protocols cannot be enforced like formal interfaces can (we’ll\n",
    "see how ABCs enforce interface conformance later in this chapter). A protocol may be\n",
    "partially implemented in a particular class, and that’s OK.\n",
    "## Python digs sequences\n",
    "In summary, given the importance of the sequence protocol, in the absence __iter__\n",
    "and __contains__ Python still manages to make iteration and the in operator work by\n",
    "invoking __getitem__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Foo:\n",
    "    def __getitem__(self, pos):\n",
    "        return range(0, 30, 10)[pos]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f=Foo()\n",
    "f[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "20 in f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "15 in f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monkey-patching to implement a protocol at run time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 1, 7, 6, 0, 5, 2, 8, 3, 4]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from random import shuffle\n",
    "l = list(range(10))\n",
    "shuffle(l)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "Card = collections.namedtuple('Card', ['rank', 'suit'])\n",
    "class FrenchDeck:\n",
    "    ranks = [str(n) for n in range(2, 11)] + list('JQKA')\n",
    "    suits = 'spades diamonds clubs hearts'.split()\n",
    "    def __init__(self):\n",
    "        self._cards = [Card(rank, suit) for suit in self.suits\n",
    "                                        for rank in self.ranks]\n",
    "    def __len__(self):\n",
    "        return len(self._cards)\n",
    "    def __getitem__(self, position):\n",
    "        return self._cards[position]\n",
    "    \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FrenchDeck only implements the immutable sequence protocol. Mutable sequences\n",
    "must also provide a __setitem__ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Card(rank='Q', suit='clubs'),\n",
       " Card(rank='8', suit='spades'),\n",
       " Card(rank='J', suit='hearts'),\n",
       " Card(rank='K', suit='hearts'),\n",
       " Card(rank='9', suit='hearts')]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def set_card(deck, position, card):\n",
    "        deck._cards[position] = card\n",
    "FrenchDeck.__setitem__ = set_card\n",
    "deck = FrenchDeck()\n",
    "shuffle(deck)\n",
    "deck[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Waterfowl and ABCs\n",
    "ABCs are meant to encapsulate very general concepts, abstractions, introduced by a\n",
    "framework — things like “a sequence” and “an exact number”. [Readers] most likely don’t\n",
    "need to write any new ABCs, just use existing ones correctly, to get 99.9% of the benefits\n",
    "without serious risk of mis-design.\n",
    "## Subclassing an ABC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "Card = collections.namedtuple('Card', ['rank', 'suit'])\n",
    "class FrenchDeck2(collections.MutableSequence):\n",
    "    ranks = [str(n) for n in range(2, 11)] + list('JQKA')\n",
    "    suits = 'spades diamonds clubs hearts'.split()\n",
    "    def __init__(self):\n",
    "        self._cards = [Card(rank, suit) for suit in self.suits\n",
    "                                        for rank in self.ranks]\n",
    "    def __len__(self):\n",
    "        return len(self._cards)\n",
    "    def __getitem__(self, position):\n",
    "        return self._cards[position]\n",
    "    def __setitem__(self, position, value): #\n",
    "        self._cards[position] = value\n",
    "    def __delitem__(self, position): #\n",
    "        del self._cards[position]\n",
    "    def insert(self, position, value): #\n",
    "        self._cards.insert(position, value)"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ABCs in the standard library\n",
    "Since Python 2.6 ABCs are available in the standard library. Most are defined in the\n",
    "collections.abc module, but there are others. You can find ABCs in the numbers and\n",
    "io packages, for example.\n",
    "### ABCs in collections.abc\n",
    "![image.png](attachment:image.png)\n",
    "Iterable, Container and Sized\n",
    "Every collection should either inherit from these ABCs or at least implement compatible\n",
    "protocols. Iterable supports iteration with __iter__, Container supports\n",
    "the in operator with __contains__ and Sized supports len() with __len__.\n",
    "Sequence, Mapping and Set\n",
    "These are the main immutable collection types, and each has a mutable subclass. A\n",
    "detailed diagram for MutableSequence is in Figure 11-2; for MutableMapping and\n",
    "MutableSet there are diagrams in Chapter 3 (Figure 3-1 and Figure 3-2).\n",
    "MappingView\n",
    "In Python 3, the objects returned from the mapping methods .items(), .keys()\n",
    "and .values() inherit from ItemsView, ValuesView and ValuesView, respectively.\n",
    "The first two also inherit the rich interface of Set, with all the operators we saw in\n",
    "“Set operations” on page 82.\n",
    "Callable and Hashable\n",
    "These ABCs are not so closely related to collections, but collections.abc was the\n",
    "first package to define ABCs in the standard library, and these two were deemed\n",
    "important enough to be included. I’ve never seen subclasses of Callable or Hasha\n",
    "ble. Their main use is to support the insinstance built-in as a safe way of determining\n",
    "whether an object is callable or hashable7.\n",
    "Iterator\n",
    "Note that iterator subclasses Iterable. We discuss this further in Chapter 14.\n",
    "### The numbers tower of ABCs\n",
    "• Number\n",
    "• Complex\n",
    "• Real\n",
    "• Rational\n",
    "• Integral\n",
    "## Defining and using an ABC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import abc\n",
    "class Tombola(abc.ABC):\n",
    "    @abc.abstractmethod\n",
    "    def load(self, iterable):\n",
    "        \"\"\"Add items from an iterable.\"\"\"\n",
    "    @abc.abstractmethod\n",
    "    def pick(self):\n",
    "        \"\"\"Remove item at random, returning it.\n",
    "            This method should raise `LookupError` when the instance is empty.\n",
    "        \"\"\"\n",
    "    def loaded(self):\n",
    "        \"\"\"Return `True` if there's at least 1 item, `False` otherwise.\"\"\"\n",
    "        return bool(self.inspect())\n",
    "    def inspect(self):\n",
    "        \"\"\"Return a sorted tuple with the items currently inside.\"\"\"\n",
    "        items = []\n",
    "        while True:\n",
    "            try:\n",
    "                items.append(self.pick())\n",
    "            except LookupError:\n",
    "                break\n",
    "        self.load(items)\n",
    "        return tuple(sorted(items))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Fake(Tombola): # ➊\n",
    "    def pick(self):\n",
    "        return 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.Fake"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Can't instantiate abstract class Fake with abstract methods load",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-0d9d37bf352d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: Can't instantiate abstract class Fake with abstract methods load"
     ]
    }
   ],
   "source": [
    "f=Fake()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ABC syntax details\n",
    "the preferred way to declare an abstract class method is:\n",
    "class MyABC(abc.ABC):\n",
    "    @classmethod\n",
    "    @abc.abstractmethod\n",
    "    def an_abstract_classmethod(cls, ...):\n",
    "        pass\n",
    "### Subclassing the Tombola ABC        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "class BingoCage(Tombola):\n",
    "    def __init__(self, items):\n",
    "        self._randomizer = random.SystemRandom()\n",
    "        self._items = []\n",
    "        self.load(items)\n",
    "    def load(self, items):\n",
    "        self._items.extend(items)\n",
    "        self._randomizer.shuffle(self._items)\n",
    "    def pick(self):\n",
    "        try:\n",
    "            return self._items.pop()\n",
    "        except IndexError:\n",
    "            raise LookupError('pick from empty BingoCage')\n",
    "    def __call__(self):\n",
    "        self.pick()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "class LotteryBlower(Tombola):\n",
    "    def __init__(self, iterable):\n",
    "        self._balls = list(iterable)\n",
    "    def load(self, iterable):\n",
    "        self._balls.extend(iterable)\n",
    "    def pick(self):\n",
    "        try:\n",
    "            position = random.randrange(len(self._balls))\n",
    "        except ValueError:\n",
    "            raise LookupError('pick from empty BingoCage')\n",
    "        return self._balls.pop(position)\n",
    "    def loaded(self):\n",
    "        return bool(self._balls)\n",
    "    def inspect(self):\n",
    "        return tuple(sorted(self._balls))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A virtual subclass of Tombola\n",
    "This is done by calling a register method on the ABC. The registered class then becomes\n",
    "a virtual subclass of the ABC, and will be recognized as such by functions like\n",
    "issubclass and isinstance, but it will not inherit any methods or attributes from the\n",
    "ABC.\n",
    "Virtual subclasses do not inherit from their registered ABCs, and\n",
    "are not checked for conformance to the ABC interface at any\n",
    "time, not even when they are instantiated. It’s up to the subclass\n",
    "to actually implement all the methods needed to avoid runtime\n",
    "errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from random import randrange\n",
    "@Tombola.register #\n",
    "class TomboList(list): #\n",
    "    def pick(self):\n",
    "        if self: #\n",
    "            position = randrange(len(self))\n",
    "            return self.pop(position) #\n",
    "        else:\n",
    "            raise LookupError('pop from empty TomboList')\n",
    "    load = list.extend #\n",
    "    def loaded(self):\n",
    "        return bool(self) #\n",
    "    def inspect(self):\n",
    "        return tuple(sorted(self))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issubclass(TomboList, Tombola)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = TomboList(range(100))\n",
    "isinstance(t, Tombola)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(__main__.TomboList, list, object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TomboList.__mro__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, inheritance is guided by a special class attribute named __mro__ — the Method\n",
    "Resolution Order. It basically lists the class and its superclasses in the order Python\n",
    "uses to search for methods\n",
    "## How the Tombola subclasses were tested\n",
    "## Usage of register in practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Struggle:\n",
    "    def __len__(self): return 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import abc\n",
    "isinstance(Struggle(), abc.Sized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issubclass(Struggle, abc.Sized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHAPTER 12 Inheritance: for good or for worse\n",
    "## Subclassing built-in types is tricky\n",
    "Before Python 2.2 it was not possible to subclass built-in types such as list or dict.\n",
    "Since then, it can be done but there is a major caveat: the code of the built-ins (written\n",
    "in C) does not call special methods overridden by user-defined classes.\n",
    "A good short description of the problem is in the documentation for PyPy, in Differences\n",
    "between PyPy and CPython, section Subclasses of built-in types:\n",
    "Officially, CPython has no rule at all for when exactly overridden method of subclasses\n",
    "of built-in types get implicitly called or not. As an approximation, these methods are\n",
    "never called by other built-in methods of the same object. For example, an overridden\n",
    "__getitem__() in a subclass of dict will not be called by e.g. the built-in get()\n",
    "method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class DoppelDict(dict):\n",
    "    def __setitem__(self, key, value):\n",
    "        super().__setitem__(key, [value] * 2)  #   *用于重复list元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd = DoppelDict(one=1)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1, 'two': [2, 2]}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd['two']=2\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': 1, 'three': 3, 'two': [2, 2]}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.update(three=3)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class AnswerDict(dict):\n",
    "    def __getitem__(self, key): #\n",
    "        return 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad = AnswerDict(a='foo')\n",
    "ad['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('foo', {'a': 'foo'})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d={}\n",
    "d.update(ad)\n",
    "d['a'],d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subclassing built-in types like dict or list or str directly is errorprone\n",
    "because the built-in methods mostly ignore user-defined\n",
    "overrides. Instead of subclassing the built-ins, derive your classes\n",
    "from UserDict, UserList and UserString from the collections\n",
    "module, which are designed to be easily extended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "class DoppelDict2(collections.UserDict):\n",
    "    def __setitem__(self, key, value):\n",
    "        super().__setitem__(key, [value] * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': [1, 1]}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd=DoppelDict2(one=1)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': [1, 1], 'two': [2, 2]}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd['two'] = 2\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': [1, 1], 'two': [2, 2], 'three': [3, 3]}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.update(three=3)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class AnswerDict2(collections.UserDict):\n",
    "    def __getitem__(self, key):\n",
    "        return 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad = AnswerDict2(a='foo')\n",
    "ad['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42, {'a': 42})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d={}\n",
    "d.update(ad)\n",
    "d['a'],d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To summarize: the problem described in this section applies only to method delegation\n",
    "within the C language implementation of the built-in types, and only affects userdefined\n",
    "classes derived directly from those types. If you subclass from a class coded in\n",
    "Python, such as UserDict or MutableMapping, you will not be troubled by this.\n",
    "## Multiple inheritance and method resolution order\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class A:\n",
    "    def ping(self):\n",
    "        print('pingA:', self)\n",
    "class B(A):\n",
    "    def pong(self):\n",
    "        print('pongB:', self)\n",
    "class C(A):\n",
    "    def pong(self):\n",
    "        print('PONGC:', self)\n",
    "class D(B, C):\n",
    "    def ping(self):\n",
    "        super().ping()\n",
    "        print('post-pingD:', self)\n",
    "    def pingpong(self):\n",
    "        self.ping()\n",
    "        super().ping()\n",
    "        self.pong()\n",
    "        super().pong()\n",
    "        C.pong(self)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pongB: <__main__.D object at 0x0000000005CC2710>\n"
     ]
    }
   ],
   "source": [
    "d = D()\n",
    "d.pong()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PONGC: <__main__.D object at 0x0000000005CC2710>\n"
     ]
    }
   ],
   "source": [
    "C.pong(d)  # You can always call a method on a superclass directly, passing the instance as an explicit argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ambiguity of a call like d.pong() is resolved because Python follows a specific order\n",
    "when traversing the inheritance graph. That order is called MRO: Method Resolution\n",
    "Order. Classes have an attribute called __mro__ holding a tuple of references to the\n",
    "superclasses in MRO order, from the current class all the way to the object class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(__main__.D, __main__.B, __main__.C, __main__.A, object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.__mro__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The recommended way to delegate method calls to superclasses is the super() built-in\n",
    "function,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pingA: <__main__.D object at 0x0000000005CC2710>\n",
      "post-pingD: <__main__.D object at 0x0000000005CC2710>\n"
     ]
    }
   ],
   "source": [
    "d.ping()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pingA: <__main__.D object at 0x0000000005CC2710>\n",
      "post-pingD: <__main__.D object at 0x0000000005CC2710>\n",
      "pingA: <__main__.D object at 0x0000000005CC2710>\n",
      "pongB: <__main__.D object at 0x0000000005CC2710>\n",
      "pongB: <__main__.D object at 0x0000000005CC2710>\n",
      "PONGC: <__main__.D object at 0x0000000005CC2710>\n"
     ]
    }
   ],
   "source": [
    "d.pingpong()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(bool, int, object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool.__mro__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple inheritance in the real world\n",
    "An extreme example of multiple inheritance in the standard library is the Tkinter GUI\n",
    "toolkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_mro(cls):\n",
    "    print(', '.join(c.__name__ for c in cls.__mro__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toplevel, BaseWidget, Misc, Wm, object\n"
     ]
    }
   ],
   "source": [
    "import tkinter\n",
    "print_mro(tkinter.Toplevel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Widget, BaseWidget, Misc, Pack, Place, Grid, object\n"
     ]
    }
   ],
   "source": [
    "print_mro(tkinter.Widget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Button, Widget, BaseWidget, Misc, Pack, Place, Grid, object\n"
     ]
    }
   ],
   "source": [
    "print_mro(tkinter.Button)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entry, Widget, BaseWidget, Misc, Pack, Place, Grid, XView, object\n"
     ]
    }
   ],
   "source": [
    "print_mro(tkinter.Entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text, Widget, BaseWidget, Misc, Pack, Place, Grid, XView, YView, object\n"
     ]
    }
   ],
   "source": [
    "print_mro(tkinter.Text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coping with multiple inheritance\n",
    "Since we don’t have a comprehensive theory, here are a few\n",
    "tips to avoid spaghetti class graphs.\n",
    "1. Distinguish interface inheritance from implementation inheritance\n",
    "When dealing with multiple inheritance it’s useful to keep straight the reasons why\n",
    "subclassing is done in the first place. The main reasons are:\n",
    "• Inheritance of interface: creates a sub-type, implying an “is-a” relationship.\n",
    "• Inheritance of implementation: avoids code duplication by reuse.\n",
    "In practice both uses are often simultaneous, but whenever you can make the intent\n",
    "clear, do it. Inheritance for code reuse is an implementation detail, and it can often be\n",
    "replaced by composition and delegation. On the other hand, interface inheritance is the\n",
    "backbone of a framework.\n",
    "2. Make interfaces explicit with ABCs\n",
    "In modern Python, if a class is designed to define an interface, it should be an explicit\n",
    "ABC. In Python ≥ 3.4 this means: subclass abc.ABC or another ABC\n",
    "3. Use mixins for code reuse\n",
    "If a class is designed to provide method implementations for reuse by multiple unrelated\n",
    "subclasses, without implying an “is-a” relationship, it should be an explicit mixin class.\n",
    "Conceptually, a mixin does not define a new type, it merely bundles methods for reuse.\n",
    "A mixin should never be instantiated, and concrete classes should not inherit only from\n",
    "a mixin. Each mixin should provide a single specific behavior, implementing few and\n",
    "very closely related methods.\n",
    "4. Make mixins explicit by naming\n",
    "There is no formal way in Python to state that a class is a mixin, so it is highly recommended\n",
    "that they are named with a …Mixin suffix. Tkinter does not follow this advice,\n",
    "but if it did, XView, would be XViewMixin, Pack would be PackMixin and so on with all\n",
    "the classes where I put the «mixin» tag\n",
    "5. An ABC may also be a mixin; the reverse is not true\n",
    "Since an ABC can implement concrete methods, it works as a mixin as well. An ABC\n",
    "also defines a type, which a mixin does not. And an ABC can be the sole base class of\n",
    "any another class, while a mixin should never be subclassed alone except by another,\n",
    "more specialized mixin — not a common arrangement in real code.\n",
    "One restriction applies to ABCs and not to mixins: the concrete methods implemented\n",
    "in an ABC should only collaborate with methods of the same ABC and its superclasses.\n",
    "This implies that concrete methods in an ABC are always for convenience, because\n",
    "everything they do an user of the class can also do by calling other methods of the ABC.\n",
    "6. Don’t subclass from more than one concrete class\n",
    "Concrete classes should have zero or at most one concrete superclass6. In other words,\n",
    "all but one of the superclasses of a concrete class should be ABCs or mixins. For example,\n",
    "in the code below, if Alpha is a concrete class, then Beta and Gamma must be ABCs or\n",
    "mixins:\n",
    "class MyConcreteClass(Alpha, Beta, Gamma):\n",
    "    \"\"\"This is a concrete class: it can be instantiated.\"\"\"\n",
    "     ... more code ...\n",
    "7. Provide aggregate classes to users\n",
    "If some combination of ABCs or mixins is particularly useful to client code, provide a\n",
    "class that brings them together in a sensible way. Grady Booch calls this an aggregate\n",
    "class.\n",
    "8. “Favor object composition over class inheritance.”\n",
    "\n",
    "# CHAPTER 13 Operator overloading: doing it right\n",
    "\n"
   ]
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